Gait-based gender classification using mixed conditional random field

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 41, Issue 5, 2011, Pages 1429-1439

  Hu, Maodi ^a   Wang, Yunhong ^a   Zhang, Zhaoxiang ^a   Zhang, De ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

Gait analysis; gender classification; human motion; Markov property; mixed conditional random field (CRF) (MCRF); shape descriptor; stance index

Indexed keywords

CONDITIONAL RANDOM FIELD; GENDER CLASSIFICATION; HUMAN MOTIONS; MARKOV PROPERTY; SHAPE DESCRIPTORS; STANCE INDEX;

GAIT ANALYSIS; MAXIMUM LIKELIHOOD; SOCIAL SCIENCES;

HIDDEN MARKOV MODELS;

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; CLUSTER ANALYSIS; FEMALE; GAIT; HUMAN; IMAGE PROCESSING; MALE; METHODOLOGY; NONLINEAR SYSTEM; PHYSIOLOGY; PROBABILITY; REPRODUCIBILITY; SEX DETERMINATION; VIDEORECORDING;

ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; FEMALE; GAIT; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MALE; MARKOV CHAINS; MODELS, BIOLOGICAL; NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SEX DETERMINATION ANALYSIS; VIDEO RECORDING;

EID: 80052899149     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2011.2149518     Document Type: Article

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